Counterfactual Phenotyping with Censored Time-to-Events

Nagpal, Chirag; Goswami, Mononito; Dufendach, Keith; Dubrawski, Artur

doi:10.1145/3534678.3539110

Cited by 17 publications

(14 citation statements)

References 32 publications

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“…Cox mixtures with heterogeneous effects (CMHE) is a recently proposed DL model that extends the Cox proportional hazards model (CPH) with the effect of confounders and treatment ( 28 ). The CPH assumes that individuals across the population have constant proportional hazards over time, which is a strong assumption.…”

Section: Methodsmentioning

confidence: 99%

Optimizing adjuvant treatment options for patients with glioblastoma

Zhu,

Wang,

Shi

et al. 2024

Front. Neurol.

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BackgroundThis study focused on minimizing the costs and toxic effects associated with unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing between radiotherapy (RT) and chemoradiotherapy (CRT), for patients based on their specific characteristics. This selection process utilized an innovative deep learning method.MethodsWe trained six machine learning (ML) models to advise on the most suitable treatment for glioblastoma (GBM) patients. To assess the protective efficacy of these ML models, we employed various metrics: hazards ratio (HR), inverse probability treatment weighting (IPTW)-adjusted HR (HRa), the difference in restricted mean survival time (dRMST), and the number needed to treat (NNT).ResultsThe Balanced Individual Treatment Effect for Survival data (BITES) model emerged as the most effective, demonstrating significant protective benefits (HR: 0.53, 95% CI, 0.48–0.60; IPTW-adjusted HR: 0.65, 95% CI, 0.55–0.78; dRMST: 7.92, 95% CI, 7.81–8.15; NNT: 1.67, 95% CI, 1.24–2.41). Patients whose treatment aligned with BITES recommendations exhibited notably better survival rates compared to those who received different treatments, both before and after IPTW adjustment. In the CRT-recommended group, a significant survival advantage was observed when choosing CRT over RT (p < 0.001). However, this was not the case in the RT-recommended group (p = 0.06). Males, older patients, and those whose tumor invasion is confined to the ventricular system were more frequently advised to undergo RT.ConclusionOur study suggests that BITES can effectively identify GBM patients likely to benefit from CRT. These ML models show promise in transforming the complex heterogeneity of real-world clinical practice into precise, personalized treatment recommendations.

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Section: Methodsmentioning

confidence: 99%

Optimizing adjuvant treatment options for patients with glioblastoma

Zhu,

Wang,

Shi

et al. 2024

Front. Neurol.

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“…The Cox Mixtures with Heterogeneous Effects (CMHE) model [32] uses a latent variable approach to capture heterogeneous treatment effects. It presupposes that individuals may fall into one of several latent clusters, each characterized by unique response patterns.…”

Section: Methodsmentioning

confidence: 99%

Personalized surgical recommendations and quantitative therapeutic insights for patients with metastatic breast cancer: Insights from deep learning

Zhu,

Zhang,

Wang

et al. 2024

Cancer Innovation

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BackgroundThe role of surgery in metastatic breast cancer (MBC) is currently controversial. Several novel statistical and deep learning (DL) methods promise to infer the suitability of surgery at the individual level.ObjectiveThe objective of this study was to identify the most applicable DL model for determining patients with MBC who could benefit from surgery and the type of surgery required.MethodsWe introduced the deep survival regression with mixture effects (DSME), a semi‐parametric DL model integrating three causal inference methods. Six models were trained to make individualized treatment recommendations. Patients who received treatments in line with the DL models' recommendations were compared with those who underwent treatments divergent from the recommendations. Inverse probability weighting (IPW) was used to minimize bias. The effects of various features on surgery selection were visualized and quantified using multivariate linear regression and causal inference.ResultsIn total, 5269 female patients with MBC were included. DSME was an independent protective factor, outperforming other models in recommending surgery (IPW‐adjusted hazard ratio [HR] = 0.39, 95% confidence interval [CI]: 0.19–0.78) and type of surgery (IPW‐adjusted HR = 0.66, 95% CI: 0.48–0.93). DSME was superior to other models and traditional guidelines, suggesting a higher proportion of patients benefiting from surgery, especially breast‐conserving surgery. The debiased effect of patient characteristics, including age, tumor size, metastatic sites, lymph node status, and breast cancer subtypes, on surgery decision was also quantified.ConclusionsOur findings suggested that DSME could effectively identify patients with MBC likely to benefit from surgery and the specific type of surgery needed. This method can facilitate the development of efficient, reliable treatment recommendation systems and provide quantifiable evidence for decision‐making.

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“…We will investigate three types of survival regression models: Cox Proportional Hazards (CPH), Deep Cox Proportional Hazards (DCPH), and Deep Survival Machines (DSM). These models are available in the open-source Auton-Survival package [17].…”

Section: Censoring-informed Survival Regression Modelsmentioning

confidence: 99%

“…Before training, the signals were transformed using the preprocessing functions provided in Auton-Survival [17], where each measurement is normalized by subtracting the mean and dividing by standard deviation. The feature vectors were then created by concatenating 10 time slices of data together.…”

Section: Performance Metricsmentioning

confidence: 99%

“…Using the open-source Auton-Survival package [17] along with data from the Alcator C-Mod [18] and DIII-D [19] tokamaks, we have benchmarked the performance of the binary classifier model disruption prediction with Random Forests [9] against the conditional Kaplan-Meier formalism (KM) [20], as well as the survival regression models Cox Proportional Hazards (CPH) [21], Deep Cox Proportional Hazards (DCPH) [15], and Deep Survival Machines (DSM) [22]. A list of the models investigated in this study is given in 1.…”

Section: Introductionmentioning

confidence: 99%

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Risk-Aware Framework Development for Disruption Prediction:Alcator C-Mod and DIII-D Survival Analysis

Keith,

Nagpal,

Rea

et al. 2024

Preprint

Self Cite

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Survival regression models can achieve longer warning times at similar receiver operating characteristic performance than previously investigated models. Survival regression models are also shown to predict the time until a disruption will occur with lower error than other predictors. Time-to-event predictions from time-series data can be obtained with a survival analysis statistical framework, and there have been many tools developed for this task which we aim to apply to disruption prediction. Using the open-source Auton-Survival package we have implemented disruption predictors with the survival regression models Cox Proportional Hazards, Deep Cox Proportional Hazards, and Deep Survival Machines. To compare with previous work, we also include predictors using a Random Forest binary classifier, and a conditional Kaplan-Meier formalism. We benchmarked the performance of these five predictors using experimental data from Alcator C-Mod and DIII-D by simulating alarms on each individual shot. We find that developing machine-relevant metrics to evaluate models is an important area for future work. While this study finds cases where disruptive conditions are not predicted, there are instances where the desired outcome is produced. Giving the plasma control system the expected time-to-disruption will allow it to determine the optimal actuator response in real-time to minimize risk of damage to the device.

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Counterfactual Phenotyping with Censored Time-to-Events

Cited by 17 publications

References 32 publications

Optimizing adjuvant treatment options for patients with glioblastoma

Optimizing adjuvant treatment options for patients with glioblastoma

Personalized surgical recommendations and quantitative therapeutic insights for patients with metastatic breast cancer: Insights from deep learning

Risk-Aware Framework Development for Disruption Prediction:Alcator C-Mod and DIII-D Survival Analysis

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